Understanding Matplotlib Pyplot
This article will demonstrate some of the key features of Matplotlib Pyplot and how you can implement them to make different types of visualizations with examples.
Pyplot is a sub-library of Matplotlib that provides a simple and intuitive interface for creating visualizations.
There are a lot of Matplotlib functions available inside the pyplot submodule, and they are commonly imported via the plt alias:
We can also assign our customized alias:
pt is now the name of the Pyplot package.
In the diagram below, sketch a line connecting (0,0) with (19,97):
Assign x1,x2 value (5,12) and y1,y2 value (16,320):
In above example we have create a simple plot using the matplotlib library in Python.
It first imports the pyplot module of matplotlib as pt and numpy module as npy.
Then it defines two arrays, x_plane and y_plane, using the numpy library. x_plane contains the x-coordinates of two points, 5 and 12, and y_plane contains the corresponding y-coordinates of those points, 16 and 320.
Finally, the plot function of pyplot is used to create a line plot with the given x and y coordinates. The show function is used to display the plot on the screen.
So, when you run this code, you should see a line plot of two points on a 2D plane with x values of 5 and 12, and corresponding y values of 16 and 320.
Types of Matplotlib Pyplot
Matplotlib Pyplot provides a wide variety of plot types, including
- Line plots
- Scatter plots
- Bar plots
A line plot displays data as a series of points connected by straight lines.
It is used to visualize trends in data over time or other continuous variables.
To create a line plot, we can use the plot() function:
A scatter plot displays data as a collection of points.
It is used to visualize the relationship between two continuous variables.
To create a scatter plot, we can use the scatter() function:
A bar plot is a plot that portray data as bars of different heights.
It is used to compare categorical data.
To create a bar plot, we can use the bar() function:
A histogram is a plot that displays the distribution of a continuous variable.
It is used to visualize the shape of the data and to identify any patterns or trends.
To create a histogram, we can use the hist() function:
Matplotlib Pyplot Benefits
- Matplotlib pyplot is a simple and intuitive tool that allows easy creation of various plots such as line plots, scatter plots, bar plots, histograms, and many others.
- It offers a broad range of customization options such as changing labels, titles, colors, line styles, marker styles, legends, and more, which makes it highly adaptable to specific requirements.
- It is highly compatible with other well-known libraries in the scientific Python ecosystem, including NumPy, Pandas, and SciPy, which makes it easy to integrate plotting functionality with data analysis and scientific computing workflows.
- It produces high-quality output in various formats, such as PNG, PDF, and SVG, enabling users to create publication-quality figures for scientific papers, reports, and presentations.